Good afternoon to everyone!! I want to make a column vector using numpy, like in Matlab when you do zeros(N,1). I've been doing it with zeros(N) and i already get an array. It maybe be the same but the results get are different with the formulas involved. Thanks ;)) ********Natali********
Hey Natali On Sat, Mar 15, 2008 at 10:30 AM, Natali Melgarejo Diaz <nmelgarejodiaz@gmail.com> wrote:
I want to make a column vector using numpy, like in Matlab when you do zeros(N,1). I've been doing it with zeros(N) and i already get an array. It maybe be the same but the results get are different with the formulas involved.
You can either create the array with the extra dimension, i.e. zeroes([N,1]) or you can use the matrix class, which always returns arrays with a minimum of two dimensions. I recommend the first approach. Regards Stéfan
Don't know if this helps, but it might: In matlab, all the typical operations assume the variables are matrices and tries to do matrix multiplication on them. Array-type operations are done using .*, .^, etc. In numpy and scipy, however, there are two types, arrays and matrices, which you can easily convert back and forth from using mat(X) and M.A or array(M). Array operations are, roughly speaking, element wise, and matrix operations do what you'd expect. It takes a little getting used to, but I find that distinguishing the types has a lot of advantages. So to get the behavior you're expecting, you probably need to declare a column matrix, so try mat(zeros(N)).T (the .T to transpose it from a row to a column). --Hoyt On Sat, Mar 15, 2008 at 10:30 AM, Natali Melgarejo Diaz <nmelgarejodiaz@gmail.com> wrote:
Good afternoon to everyone!!
I want to make a column vector using numpy, like in Matlab when you do zeros(N,1). I've been doing it with zeros(N) and i already get an array. It maybe be the same but the results get are different with the formulas involved. Thanks ;))
********Natali******** _______________________________________________ SciPy-user mailing list SciPy-user@scipy.org http://projects.scipy.org/mailman/listinfo/scipy-user
On 15/03/2008, Hoyt Koepke <hoytak@gmail.com> wrote:
Don't know if this helps, but it might:
In matlab, all the typical operations assume the variables are matrices and tries to do matrix multiplication on them. Array-type operations are done using .*, .^, etc. In numpy and scipy, however, there are two types, arrays and matrices, which you can easily convert back and forth from using mat(X) and M.A or array(M). Array operations are, roughly speaking, element wise, and matrix operations do what you'd expect. It takes a little getting used to, but I find that distinguishing the types has a lot of advantages.
So to get the behavior you're expecting, you probably need to declare a column matrix, so try mat(zeros(N)).T (the .T to transpose it from a row to a column).
I would put it differently. Numpy arrays do not always have two or more dimensions. A one-dimensional array is neither a row nor a column vector, it's just an array of numbers. Also, the operations on arrays are all elementwise, like matlab's .* and .^ operators. If you want matrix multiplication, use the function dot(). If you're doing so many matrix operations that all those dot()s become cumbersome, you may want to consider using the matrix() wrapper around arrays; without affecting the underlying data, this rebinds * to do matrix multiplication, and enforces at-least-two-dimensionality. But like all wrappers, this is imperfect, and you can expect to stumble over a few inconveniences (functions that inadvertently convert your matrices to arrays, for example), so I recommend getting used to arrays first, and only switching to matrices if absolutely necessary. Anne
participants (4)
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Anne Archibald -
Hoyt Koepke -
Natali Melgarejo Diaz -
Stéfan van der Walt